Search Results for author:
Found 15 papers, 3 papers with code
CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models
We motivate and introduce CHARD: Clinical Health-Aware Reasoning across Dimensions, to investigate the capability of text generation models to act as implicit clinical knowledge bases and generate free-flow textual explanations about various health-related conditions across several dimensions.
Template Filling for Controllable Commonsense Reasoning
To enable better controllability, we propose to study the commonsense reasoning as a template filling task (TemplateCSR) -- where the language models fills reasoning templates with the given constraints as control factors.
Learning Lexical Entries for Robotic Commands using Crowdsourcing
Robotic commands in natural language usually contain various spatial descriptions that are semantically similar but syntactically different.
Privacy-Preserving Multi-Document Summarization
State-of-the-art extractive multi-document summarization systems are usually designed without any concern about privacy issues, meaning that all documents are open to third parties.
Extending a Single-Document Summarizer to Multi-Document: a Hierarchical Approach
The increasing amount of online content motivated the development of multi-document summarization methods.
Resources for the Detection of Conventionalized Metaphors in Four Languages
The extraction rules operate on the output of a dependency parser and identify the grammatical configurations (such as a verb with a prepositional phrase complement) that are likely to contain conventional metaphors.
Ensemble Detection of Single & Multiple Events at Sentence-Level
Event classification at sentence level is an important Information Extraction task with applications in several NLP, IR, and personalization systems.
Co-Multistage of Multiple Classifiers for Imbalanced Multiclass Learning
In this work, we propose two stochastic architectural models (CMC and CMC-M) with two layers of classifiers applicable to datasets with one and multiple skewed classes.
Supervised Topical Key Phrase Extraction of News Stories using Crowdsourcing, Light Filtering and Co-reference Normalization
We also experimented with 2 forms of document pre-processing that we call light filtering and co-reference normalization.
Recognition of Named-Event Passages in News Articles
We extend the concept of Named Entities to Named Events - commonly occurring events such as battles and earthquakes.
Key Phrase Extraction of Lightly Filtered Broadcast News
Key phrases are often used to index the document or as features in further processing.
